Test–retest reliability of brain morphology estimates

Madan, Christopher R.; Kensinger, Elizabeth A.

doi:10.1007/s40708-016-0060-4

Cited by 107 publications

(126 citation statements)

References 111 publications

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“…With regard to our empirical results, deviation analyses suggested a high overall test-retest stability of the fractal dimension estimates (approximately 95 %) across both the MASSIVE and the MSC data sets. This is in accordance with a recent reliability study of brain morphology estimates in two openaccess data sets by Madan and Kensinger (Madan and Kensinger, 2017) who found that regional fractal dimensionality as computed by both dilation and box-counting methods was generally very high and comparable to the reliability of gyrication indices, while it was in fact superior to volumetric measures such as cortical thickness. Similarly, Goñi and colleagues analyzed the fractal properties of the pial surface, the gray matter / white matter boundary and the cortical ribbon and white matter volumes in MRI data from dierent imaging centers and found a high within-subject reproducibility with regionspecic patterns of individual variability (Goñi et al, 2013).…”

Section: Discussionsupporting

confidence: 91%

Evaluation of the 3D fractal dimension as a marker of structural brain complexity in multiple-acquisition MRI

Krohn

Froeling

Leemans

et al. 2017

Preprint

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Keywords: fractal analysis, structural brain complexity, MRI biomarker, computational image analysis 1 All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/124206 doi: bioRxiv preprint first posted online Apr. 5, 2017; AbstractFractal analysis, i.e. the estimation of an object's fractal dimension (FD) as a marker of its morphometric complexity, has attracted increasing interest as a versatile tool for the analysis of structural neuroimaging data in both health and disease. However, a number of important methodological questions regarding fractal analysis in magnetic resonance images have so far remained unaddressed. This includes the stability of the FD over repeated within-subject measurements, i.e. the susceptibility of fractal analysis to noise, a formal assessment of its sampling distribution, and the impact of image acquisition and processing parameters. Importantly, fractal analysis has not yet been explored in detail in T2 contrast images. To address these issues, we analyzed structural images from the recently published MASSIVE data set (Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation). We conduct a fine-grained stratification of image parameters, leading to 32 distinct analysis groups as a combination of image contrast, spatial resolution, segmentation procedures, tissue type, and image complexity. We estimate 3D tissue models based on the thus obtained input volumes and compute the FDs as the box-counting regression on these models. Furthermore, we present a detailed deviation analysis including resampling methods, composite normality assessment, outlier detection, and multivariate comparisons to establish the susceptibility of the FD to noise. We find that in both T1 and T2 contrasts, the FD of gray matter (GM) segmentations was generally higher than in white matter volumes (WM). FDs in both image contrasts were sampled in comparable range and showed similar responses to processing parameters, e.g. as regards the effects of binary vs. partial volume segmentation and a decrease in FD by image skeletization. Lower spatial resolution invariably resulted in decreased FDs in unskeletized images, while the response depended on the segmentation procedure in image skeletons. Furthermore, in multiple measurements, the FD can be assumed to be sampled from an underlying normal distribution. We tested different options for a sensible within-group deviation criterion and found that outlier detection by Grubbs testing and a 2 standard-deviation interval around the sample mean performed very well in this regard. Even with the more conservative threshold, the overall robustness of the FD to noise was well above 90 %. Most deviations were found in T1-weighted images, and binarized image skeletons were most susceptible to deviations. Importantly, our analysis was able to detect sample-w...

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Section: Discussionsupporting

confidence: 91%

Evaluation of the 3D fractal dimension as a marker of structural brain complexity in multiple-acquisition MRI

Krohn

Froeling

Leemans

et al. 2017

Preprint

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“…Since cortical thickness has been reported as highly reliable 55 , the negative correlation between gray matter thickness and sulcal width shown in our study ( Figure S22 ) supports the hypothesis that inter-subject variability, and by consequence sulcal labeling performance, affects the reliability of sulcal shape descriptors in addition to segmentation performance.…”

Section: Discussionsupporting

confidence: 86%

The reliability and heritability of cortical folds and their genetic correlations across hemispheres

Pizzagalli¹,

Auzias²,

Yang³

et al. 2019

Preprint

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The structure of the brain's cortical folds varies considerably in human populations. Specific patterns of cortical variation arise with development and aging, and cortical traits are partially influenced by genetic factors. The degree to which genetic factors affect cortical folding patterning remains unknown, yet may be estimated with large-scale in-vivo brain MRI. Using multiple MRI datasets from around the world, we estimated the reliability and heritability of sulcal morphometric characteristics including length, depth, width, and surface area, for 61 sulci per hemisphere of the human brain. Reliability was assessed across four distinct test-retest datasets. We meta-analyzed the heritability across three independent family-based cohorts (N > 3,000), and one cohort of largely unrelated individuals (N~9,000) to examine the robustness of our findings. Reliability was high ( interquartile range for ICC: 0.65-0.85) for sulcal metrics. Most sulcal measures were moderately to highly heritability (0.3-0.7). These genetic influences vary regionally, with the earlier forming sulci having higher heritability estimates. The central sulcus, the subcallosal and the collateral fissure were the most highly heritable regions. For some frontal and temporal sulci, left and right genetic influences did not completely overlap, suggesting some lateralization of genetic effects on the cortex.

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“…A trivial difference between scan-rescan PVS maps was observed, which are most likely due to 1) segmentation imperfection and image intensity differences of scan-rescan signal (e.g. due to subject motion) (Madan and Kensinger, 2017;Reuter et al, 2015), 2) normal physiological changes of PVS in the same subject, such as potential effects of time-of-day and hydration on morphometric estimates of PVS (Dickson et al, 2005;Kempton et al, 2009;Trefler et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Image processing approaches to enhance perivascular space visibility and quantification using MRI

Sepehrband

Barisano

Sheikh‐Bahaei

et al. 2019

Preprint

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Word count: 4080 Number of figures: 8 Number of tables: 1 AbstractImaging the perivascular spaces (PVS), also known as Virchow-Robin space, has significant clinical value, but there remains a need for neuroimaging techniques to improve mapping and quantification of the PVS. Current technique for PVS evaluation is a scoring system based on visual reading of visible PVS in regions of interest, and often limited to large caliber PVS.Enhancing the visibility of the PVS could support medical diagnosis and enable novel neuroscientific investigations. Increasing the MRI resolution is one approach to enhance the visibility of PVS but is limited by acquisition time and physical constraints. Alternatively, image processing approaches can be utilized to improve the contrast ratio between PVS and surrounding tissue. Here we combine T1-and T2-weighted images to enhance PVS contrast, intensifying the visibility of PVS. The Enhanced PVS Contrast (EPC) was achieved by combining T1-and T2-weighted images that were adaptively filtered to remove non-structured highfrequency spatial noise. EPC was evaluated on healthy young adults by presenting them to two expert readers and also through automated quantification. We found that EPC improves the conspicuity of the PVS and aid resolving a larger number of PVS. We also present a highly reliable automated PVS quantification approach, which was optimized using expert readings.

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Test–retest reliability of brain morphology estimates

Cited by 107 publications

References 111 publications

Evaluation of the 3D fractal dimension as a marker of structural brain complexity in multiple-acquisition MRI

Evaluation of the 3D fractal dimension as a marker of structural brain complexity in multiple-acquisition MRI

The reliability and heritability of cortical folds and their genetic correlations across hemispheres

Image processing approaches to enhance perivascular space visibility and quantification using MRI

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